DGX B300 Core Computing Architecture: A Deep Dive into NVIDIA’s Next-Gen AI Supercomputing Platform
The rapid evolution of AI workloads—especially generative AI, large language models (LLMs), and reasoning systems—has driven the need for unprecedented computing power. Recently, Lilly, a global pharmaceutical and life sciences leader, announced the deployment of the world’s first NVIDIA DGX SuperPOD powered by DGX B300 systems. This marks one of the largest and most powerful enterprise AI factories operated entirely in-house.
The deployment integrates 1,016 NVIDIA Blackwell Ultra GPUs, delivering more than 9 quintillion calculations per second, showcasing the massive scale of modern AI infrastructure. However, beyond this milestone, the DGX B300 architecture itself represents a foundational blueprint for enterprises building high-performance AI platforms.
In this article, we explore the core computing architecture of DGX B300, covering GPU design, memory architecture, interconnect fabric, system engineering, and real-world deployment considerations.
Compute Architecture: NVIDIA Blackwell Ultra (B300)
The DGX B300 platform is engineered to address the demands of next-generation AI workloads. Modern AI models are larger, require longer context windows, and increasingly depend on reasoning-based inference. NVIDIA’s Blackwell Ultra GPUs are purpose-built to meet these requirements with sustained throughput, efficient precision formats, and enhanced attention acceleration.
Each DGX B300 system integrates eight Blackwell Ultra GPUs, functioning as a tightly coupled compute complex rather than independent accelerators.
Dual-Die GPU Design for Extreme Compute Density
Blackwell Ultra introduces a dual-reticle GPU architecture, with each B300 GPU containing 208 billion transistors across two silicon dies. These dies are connected using NVIDIA’s High-Bandwidth Interface (NV-HBI), delivering up to 10 TB/s of on-package bandwidth.
From a software perspective, the two dies operate as a single logical GPU within the CUDA programming model. This ensures developers can scale compute density without added complexity in scheduling or memory management.
NVFP4 Precision and Massive Compute Throughput
To optimize inference efficiency, Blackwell Ultra introduces the NVFP4 precision format, designed specifically for transformer-based models. Each GPU can deliver up to 15 petaFLOPS of dense compute using NVFP4.
Compared to FP8, NVFP4 reduces memory usage by approximately 1.8×, allowing larger models and activations to remain resident in GPU memory. For production environments, this translates into higher throughput, lower latency, and improved cost efficiency.
Accelerated Reasoning and Attention Processing
Reasoning models heavily rely on attention layers, especially with long context windows. Blackwell Ultra doubles the throughput of Special Function Units (SFUs), enabling up to 2× faster attention-layer performance.
This enhancement significantly reduces inference latency and improves utilization for workloads dominated by attention operations, such as LLMs and multimodal AI systems.
Memory Architecture: Unified HBM3e at Scale
Memory performance is critical for large AI models. DGX B300 combines high-capacity GPU memory with ultra-high bandwidth, ensuring compute engines remain fully utilized even for trillion-parameter-scale workloads.
High-Capacity GPU Memory for Massive Models
Each DGX B300 system offers 2.3 TB of total GPU memory, with 288 GB of HBM3e per GPU. This represents a 3.6× increase over the H100 generation.
This capacity enables models with 300+ billion parameters to run entirely in GPU memory, reducing reliance on system RAM and storage tiers. Keeping models resident in GPU memory significantly improves performance and stability.
Ultra-High Memory Bandwidth for AI Workloads
Each B300 GPU uses 12-high HBM3e stacks, delivering up to 8 TB/s of memory bandwidth. This ensures compute engines are continuously fed with data, preventing stalls during training or inference.
The combination of high capacity and bandwidth makes DGX B300 ideal for large-scale training, real-time inference, and complex reasoning workloads.
Interconnect and Networking Architecture
As AI systems scale, data movement becomes as critical as compute power. DGX B300 integrates high-speed interconnects for both scale-up (within a system) and scale-out (across clusters) performance.
Intra-System Scale-Up with NVLink 5
Inside a single DGX B300 system, the eight GPUs are connected via fifth-generation NVIDIA NVLink. Each GPU achieves 1.8 TB/s bidirectional bandwidth, enabling efficient memory sharing and distributed computation.
This allows applications to treat the GPUs as one unified compute resource, eliminating bottlenecks in large-scale AI workloads such as long-context inference and generative AI pipelines.
Inter-System Scale-Out Networking
To scale across clusters and AI factories, DGX B300 includes:
Eight OSFP ports supporting up to 800 Gb/s InfiniBand or Ethernet via NVIDIA ConnectX-8 SuperNICs
Two dual-port NVIDIA BlueField-3 DPUs for storage acceleration, infrastructure management, and security isolation
This architecture ensures low-latency, high-throughput communication, essential for distributed AI training and inference at enterprise scale.
System Design and Physical Engineering
DGX B300 is engineered not only for performance but also for data center reliability, serviceability, and efficiency.
Chassis and Form Factor
The system uses a 10 Rack Unit (RU) chassis designed for enterprise data centers:
Front-accessible I/O for simplified cabling and maintenance
Rear thermal access for efficient cooling management
Compact 10 RU form factor for maximum compute density in standard racks
Power and Infrastructure Compatibility
DGX B300 is designed for flexible deployment:
Power consumption: ~14.5 kW per system
Power options: AC/PDU and DC/busbar configurations
This flexibility enables deployment in both new and existing data centers without extensive infrastructure changes.
CPU and System Memory
While GPUs drive AI compute, DGX B300 includes a powerful supporting platform:
Two Intel Xeon Platinum 6776P CPUs for orchestration and preprocessing
2 TB DDR5 system memory (expandable to 4 TB) for data pipelines and system-level workloads
This ensures smooth end-to-end AI workflows—from ingestion and preprocessing to training and inference.
Why DGX B300 Matters for Enterprise AI
DGX B300 represents a significant leap in AI infrastructure, combining:
Blackwell Ultra GPUs for extreme compute
HBM3e memory for large-model performance
NVLink and high-speed networking for scalability
Enterprise-grade system engineering for reliability
However, unlocking its full potential requires careful planning across power, cooling, networking, and orchestration layers.
DGX B300 Deployment Guidance via Uvation Marketplace
Deploying DGX B300 is not just a hardware upgrade—it is a strategic infrastructure transformation. Organizations must align data center readiness, networking, storage, orchestration, and AI workloads to maximize ROI.
[Uvation Marketplace] provides a centralized platform to simplify DGX B300 adoption and deployment. Through the marketplace, organizations can:
Explore system configurations tailored to AI workloads
Assess power, cooling, and infrastructure readiness
Plan integration with existing networking and storage stacks
Access expert advisory support for performance optimization
For enterprises seeking end-to-end guidance, Uvation experts offer free consultations to align DGX B300 strategies with long-term AI goals and business outcomes.
Final Thoughts
DGX B300 is more than a GPU system—it is the foundation for next-generation AI factories. With unmatched compute density, memory bandwidth, and interconnect performance, it enables enterprises to scale AI workloads that were previously impractical.
With proper planning and deployment support, organizations can transform this advanced hardware into sustained AI performance, scalability, and competitive advantage—and platforms like Uvation Marketplace can accelerate that journey.
